STAT154 Project2

Split method 1 By label

traintest_split<- function(data){
  res<-list()
  trainIndex <- createDataPartition(data$label, p = .8, 
                                  list = FALSE, 
                                  times = 1)
  res$train<- data[ trainIndex,]
  res$test<-  data[-trainIndex,]
  return(res)
}
trainval_split<- function(data){
  res<-list()
  valIndex <- createDataPartition(data$label, p = .2, 
                                  list = FALSE, 
                                  times = 1)
  res$val<- data[ valIndex,]
  res$train<-  data[-valIndex,]
  return(res)
}

##image 1
#Test-train split
train1_label_split1<- traintest_split(image1)$train
# get test from train-test split
test_label_split1<- traintest_split(image1)$test

#Train-val split ---get val
val_label_split1<- trainval_split(train1_label_split1)$val
#Get train
train_label_split1<-trainval_split(train1_label_split1)$train

##image 2
train2_label_split2<- traintest_split(image2)$train
# get test from train-test split
test_label_split2<- traintest_split(image2)$test
#Train-val split ---get val
val_label_split2<- trainval_split(train2_label_split2)$val
#Get train
train_label_split2<-trainval_split(train2_label_split2)$train

##image 3
train3_label_split3<- traintest_split(image3)$train
# get test from train-test split
test_label_split3<- traintest_split(image3)$test

#Train-val split ---get val
val_label_split3<- trainval_split(train3_label_split3)$val
#Get train
train_label_split3<-trainval_split(train3_label_split3)$train

method1_train<-rbind(train_label_split1,train_label_split2,train_label_split3)
method1_val<-rbind(val_label_split1,val_label_split2,val_label_split3)
method1_test<- rbind(test_label_split1,test_label_split2,test_label_split3)
method1_train_val<-rbind(method1_train,method1_val)
method1_train_val$label<-factor(method1_train_val$label)

Split method 2 By block

#Check Duplicates
image1 %>% distinct()
grid_row <- 10
grid_col <- 10
datasplit<- function(grid_row, grid_col, image){
  ret <- list()
  train_data <- data.frame()
  val_data <- data.frame()
  test_data <- data.frame()
  min_y_cor <- min(image$y_coordinate)
  min_x_cor <- min(image$x_coordinate)
  max_y_cor <- max(image$y_coordinate)
  max_x_cor <- max(image$x_coordinate)
  divide_row <- seq(min_y_cor,max_y_cor+1,(max_y_cor+1-min_y_cor)/(grid_row-1))
  divide_col <- seq(min_x_cor,max_x_cor+1,(max_x_cor+1-min_x_cor)/(grid_col-1))
  for (i in 1:length(divide_row)){
    for (j in 1:length(divide_col)){
      chunk <- image[image$y_coordinate>=divide_row[i] & image$y_coordinate<divide_row[i+1] & 
                       image$x_coordinate>=divide_col[j] & image$x_coordinate<divide_col[j+1], ]
      test_and_val <- floor(nrow(chunk)*0.4)
      test_and_val_ind <- sample(seq_len(nrow(chunk)), size = test_and_val)
      val_size <- floor(test_and_val/2)
      val_ind_ind <- sample(length(test_and_val_ind), size = val_size)
      val_ind <- test_and_val_ind[val_ind_ind]
      test_ind <- test_and_val_ind[-val_ind_ind]
      test<- chunk[test_ind, ]
      val<- chunk[val_ind, ]
      train<- chunk[-test_and_val_ind, ]
      train_data = rbind(train_data,train)
      test_data = rbind(test_data,test)
      val_data = rbind(val_data,val)
    }
  }
  ret$training <- train_data
  ret$validation <- val_data
  ret$test <- test_data
  return(ret)
}

split_result_1 <- datasplit(10,10,image1)
split_result_2 <- datasplit(10,10,image2)
split_result_3 <- datasplit(10,10,image3)
train_total<- rbind(split_result_1$training,split_result_2$training,split_result_3$training)
val_total<- rbind(split_result_1$validation,split_result_2$validation,split_result_3$validation)
test_total<- rbind(split_result_1$test,split_result_2$test,split_result_3$test)
train_total$picture<- NULL
val_total$picture<- NULL
test_total$picture<-NULL
train_total$label<-factor(train_total$label)

Visualization

train_val<- rbind(train_total, val_total)
train_val$label<- as.factor(train_val$label)
test_total$label<- as.factor(test_total$label)
#visualization by using boxplot
A<-ggplot(train_val)+geom_boxplot(aes(x=factor(label), y= y_coordinate,
                                      color= factor(label)))+ theme_bw()+ 
  ggtitle("y_coordinate and label")+xlab("label")+
  theme(plot.title = element_text(size = rel(1), 
                                  vjust = 1.5,face="bold.italic"))+
  theme(legend.position="none")

B<-ggplot(train_val)+geom_boxplot(aes(x=factor(label), y= x_coordinate,
                                      color= factor(label)))+ theme_bw()+ 
  ggtitle("x_coordinate and label")+theme(legend.position="none")+
  xlab("label")+theme(plot.title = element_text(size = rel(1), 
                                                vjust = 1.5,face="bold.italic"))

C<-ggplot(train_val)+geom_boxplot(aes(x=factor(label), y= NDAI,
                                      color= factor(label)))+ theme_bw()+ 
  ggtitle("label and NDAI")+theme(legend.position="none")+
  xlab("label")+theme(plot.title = element_text(size = rel(1),
                                                vjust = 1.5,face="bold.italic"))

D<-ggplot(train_val)+geom_boxplot(aes(x=factor(label), y= SD,color= factor(label)))+ theme_bw()+ 
  ggtitle("label and SD")+theme(legend.position="none")+
  xlab("label")+theme(plot.title = element_text(size = rel(1),
                                                vjust = 1.5,face="bold.italic"))

E<-ggplot(train_val)+geom_boxplot(aes(x=factor(label), y= CORR,color= factor(label)))+ theme_bw()+ 
  ggtitle("label and CORR")+theme(legend.position="none")+
  xlab("label")+theme(plot.title = element_text(size = rel(1),
                                                vjust = 1.5,face="bold.italic"))

G<-ggplot(train_val)+geom_boxplot(aes(x=factor(label), y= DF_angle,
                                      color= factor(label)))+ theme_bw()+ 
  theme(legend.position="none")+
  ggtitle("label and DF_angle")+
  xlab("label")+theme(plot.title = element_text(size = rel(1), 
                                                vjust = 1.5,face="bold.italic"))

H<-ggplot(train_val)+geom_boxplot(aes(x=factor(label), y= CF_angle,
                                      color= factor(label)))+ theme_bw()+
  theme(legend.position="none")+
  ggtitle("label and CF_angle")+
  xlab("label")+theme(plot.title = element_text(size = rel(1), 
                                                vjust = 1.5,face="bold.italic"))

I<-ggplot(train_val)+geom_boxplot(aes(x=factor(label), y= BF_angle,
                                      color= factor(label)))+ theme_bw()+
  theme(legend.position="none")+
  ggtitle("label and BF_angle")+
  xlab("label")+theme(plot.title = element_text(size = rel(1), 
                                                vjust = 1.5,face="bold.italic"))

J<-ggplot(train_val)+geom_boxplot(aes(x=factor(label), y= AF_angle,
                                      color= factor(label)))+ theme_bw()+ 
  theme(legend.position="none")+
  ggtitle("label and AF_angle")+
  xlab("label")+theme(plot.title = element_text(size = rel(1), 
                                                vjust = 1.5,face="bold.italic"))

K<-I<-ggplot(train_val)+geom_boxplot(aes(x=factor(label), y= AN_angle,
                                         color= factor(label)))+ theme_bw()+ 
  ggtitle("label and AN_angle")+theme(legend.position="none")+
  xlab("label")+theme(plot.title = element_text(size = rel(1),
                                                vjust = 1.5,face="bold.italic"))
 
plot_grid(A, B,C,D,E,G,H,I,J,K,nrow=3, align="h")

Quantitative

summary(lm(as.numeric(label)~NDAI, data = train_val))$r.squared

## [1] 0.5745847

summary(lm(as.numeric(label)~CORR, data = train_val))$r.squared

## [1] 0.3038954

summary(lm(as.numeric(label)~AF_angle, data = train_val))$r.squared

## [1] 0.2569522

summary(lm(as.numeric(label)~x_coordinate, data = train_val))$r.squared

## [1] 0.3260832

summary(lm(as.numeric(label)~y_coordinate, data = train_val))$r.squared

## [1] 0.07864526

summary(lm(as.numeric(label)~SD, data = train_val))$r.squared

## [1] 0.1906087

summary(lm(as.numeric(label)~DF_angle, data = train_val))$r.squared

## [1] 0.0001169845

summary(lm(as.numeric(label)~CF_angle, data = train_val))$r.squared

## [1] 0.07981812

summary(lm(as.numeric(label)~AN_angle, data = train_val))$r.squared

## [1] 0.2542656

Model 1: Logistic Regression

# Compute CV loss
seed = 123
K = 5
source("CVgeneric.R")
cv_result <- CVgeneric("logistic", c("y_coordinate", "x_coordinate", 
                                     "NDAI", "SD", "CORR", "DF_angle", 
                                     "CF_angle", "BF_angle", "AF_angle", 
                                     'AN_angle'), "label",
                       K, data=train_val, loss= accuracy, seed)
set.seed(cv_result$seed)
cv_result_method_2 <- CVgeneric("logistic", c("y_coordinate", "x_coordinate",
                                              "NDAI", "SD", "CORR", "DF_angle", 
                                              "CF_angle", "BF_angle", "AF_angle", 
                                              'AN_angle'), "label", 
                                K, data=method1_train_val, loss= accuracy, seed)
# Use data based on the best folds
train_data = train_val[-cv_result$index, ]
# Train logistic model
logistic_model<- train(label ~ .,  data=train_data, method="glm", family="binomial")
# Test accuracy
predicted.classes <- logistic_model %>% predict(test_total[,-3])
mean(predicted.classes == test_total$label)

## [1] 0.8964581

# Other error metrics
# F1 Score
F1_Score(as.numeric(predicted.classes), as.numeric(test_total$label))

## [1] 0.9149075

# Sensitivity
Sensitivity(as.numeric(predicted.classes), as.numeric(test_total$label))

## [1] 0.9189178

# Find the best cut off point
predictions<- data.frame(predict(logistic_model, newdata =test_total[,-3],
                                 type= "prob" ))
colnames(predictions) <- c(-1,1)
pred <- prediction(predictions$`1`, test_total$label)
perf <- performance(pred, "acc")
plot(perf, avg= "vertical", spread.estimate="boxplot",
     show.spread.at= seq(0.1, 0.9, by=0.1), 
     main="Logistic regression cutoff and performance tradeoff")

index<-which.max(slot(perf, "y.values")[[1]])
max<-slot(perf, "x.values")[[1]][index]

perf <- performance(pred, "tpr", "fpr")
# ROC curve plot
plot(perf, colorize=T, print.cutoffs.at=c(max),
     text.adj=c(1.5,0.2),
     avg="threshold", lwd=3,
     main= "Roc curve for logistic regression")
abline(a=0,b=1)

#### Model 2: LDA

# Compute CV loss
cv_result_lda<-CVgeneric("lda", c("y_coordinate", "x_coordinate", 
                                  "NDAI", "SD", "CORR", "DF_angle", 
                                  "CF_angle", "BF_angle", "AF_angle", 'AN_angle'),
                         "label",
                         K = 5, train_val, loss = precision, seed)
cv_result_lda_method_2<-CVgeneric("lda", c("y_coordinate", "x_coordinate", 
                                           "NDAI", "SD", "CORR", "DF_angle",
                                           "CF_angle", "BF_angle", "AF_angle", 'AN_angle'),
                        "label",
                        K = 5, method1_train_val, loss = precision, seed)
# Use data based on the best folds
train_data = train_val[-cv_result_lda$index, ]
# Fit LDA Model
lda.model = lda(factor(label)~., data=train_val)
lda_pred<-predict(lda.model, newdata=test_total[,-3])
lda_pre2<-predict(lda.model, newdata=test_total[,-3], type=  "prob")
  
#Other error metrics
#F1 Score
F1_Score(as.numeric(lda_pred$class), as.numeric(test_total$label))

## [1] 0.9172956

#Sensitivity
Sensitivity(as.numeric(lda_pred$class), as.numeric(test_total$label))

## [1] 0.9292314

#Accuracy of the model
mean(lda_pred$class == test_total$label)

## [1] 0.9002067

# Find the best cut off point
predictions<- data.frame(predict(lda.model, newdata =test_total[,-3] ))
colnames(predictions) <- c("label",-1,1)
pred <- prediction(predictions$`1`, test_total$label)
perf <- performance(pred, "acc")
plot(perf, avg= "vertical", spread.estimate="boxplot", show.spread.at= seq(0.1, 0.9, by=0.1), main="LDA cutoff and performance tradeoff")

index<-which.max(slot(perf, "y.values")[[1]])
max<-slot(perf, "x.values")[[1]][index]
# ROC Curve
perf <- performance(pred, "tpr", "fpr")
plot(perf, colorize=T, print.cutoffs.at=c(max), text.adj=c(1,0), avg="threshold", lwd=3, main= "LDA curve for ROC")
abline(a=0,b=1)

Model 3: Decision Tree

# Delete unused column
method1_train_val$picture<-NULL
# Compute CV loss for first method split
seed = 123
K = 5
cv_result_decision_tree <- CVgeneric("decision tree", 
                                     c("y_coordinate",  "x_coordinate",  "NDAI","SD","CORR","DF_angle","CF_angle","BF_angle","AF_angle",'AN_angle'),"label",K,data=train_val,loss= accuracy, seed)
# Use data based on the best folds for the first method split
train_data = train_val[-cv_result_decision_tree$index, ]

# Train the decision tree model
tree = rpart(label ~ ., data=train_data,maxdepth =10, minsplit= 10)
# Visualize the decision tree
fancyRpartPlot(tree)

# Train the decision tree model without the geographic features
tree_no_xy = rpart(label ~ NDAI+SD+CORR+DF_angle+CF_angle+BF_angle+AF_angle+AN_angle , data=train_data,maxdepth =10, minsplit= 10)
# Visualize the decision tree model without the geographic features
fancyRpartPlot(tree_no_xy)

# Accuracy for decision tree model without the geographic features
tree.pred_no_xy = predict(tree_no_xy, newdata=test_total[,-3],type = 'class')
mean(tree.pred_no_xy==test_total$label)

## [1] 0.9060458

# Accuracy for  decision tree model WITH the geographic features
tree.pred = predict(tree, newdata=test_total[,-3],method = 'response')
tree.pred2 = predict(tree, newdata=test_total[,-3],type = 'class')
mean(tree.pred2==test_total$label)

## [1] 0.9227461

#Other error metrics
#F1 Score
F1_Score(as.numeric(tree.pred2), as.numeric(test_total$label))

## [1] 0.9343677

#Sensitivity
Sensitivity(as.numeric(tree.pred2), as.numeric(test_total$label))

## [1] 0.9715559

# Find the best cut off point
predictions<- data.frame(predict(tree, newdata =test_total[,-3], type= "prob" ))
colnames(predictions) <- c(-1,1)
pred <- prediction(predictions$`1`, test_total$label)
perf <- performance(pred, "acc")
plot(perf, avg= "vertical", spread.estimate="boxplot", 
     show.spread.at= seq(0.1, 0.9, by=0.1), 
     main="Decision Tree cutoff and performance tradeoff")

index<-which.max(slot(perf, "y.values")[[1]])
max<-slot(perf, "x.values")[[1]][index]
perf <- performance(pred, "tpr", "fpr")
# ROC Curve
plot(perf, colorize=T, print.cutoffs.at=max, text.adj=c(1,0), avg="threshold", 
     lwd=3, 
     main= "Roc curve for Decision Tree")
abline(a=0,b=1)

# Decision Tree feature importance (4a preparation)
tree_importance<-as.data.frame(varImp(tree))
tree_importance$importance<-rownames(tree_importance)
tree_importance <- tree_importance %>% arrange(desc(Overall))

# Compute CV loss for second method data split
cv_result_decision_tree_method2 <- CVgeneric("decision tree", 
                                             c("y_coordinate", "x_coordinate", 
                                               "NDAI", "SD", "CORR", "DF_angle", 
                                               "CF_angle", "BF_angle", "AF_angle",
                                               'AN_angle'), "label",K, 
                                             data=method1_train_val, 
                                             loss= accuracy, seed)
train_data_tree_2 = method1_train_val[-cv_result_decision_tree_method2$index, ]
tree2 = rpart(label ~ ., data=train_data_tree_2,maxdepth =10, minsplit= 10)
tree.pred_method_2 <- predict(tree2, newdata=test_total[,-3],type = 'class')

Model 4:Random Forest

seed = 123
K = 5
# Compute CV loss for first method split
cv_result_rf <- CVgeneric("random forest", c("y_coordinate", 
                                             "x_coordinate", 
                                             "NDAI", "SD", "CORR", 
                                             "DF_angle", "CF_angle", 
                                             "BF_angle", "AF_angle", 
                                             'AN_angle'), "label", K, 
                          data=train_val, loss= accuracy, seed)
# Use the best folds on the training set
train_data_rf = train_val[-cv_result_rf$index, ]
# Choose the best hyperparameter of ntry which is the Number of variables randomly sampled as candidates at each split.
Random_forest_hyperparameter_tune<-tuneRF(x =train_data[,-3],
              y = as.factor(train_data[,3]),
              ntreeTry = 50)

## mtry = 3  OOB error = 0.49% 
## Searching left ...
## mtry = 2     OOB error = 0.62% 
## -0.2664625 0.05 
## Searching right ...
## mtry = 6     OOB error = 0.39% 
## 0.2143951 0.05 
## mtry = 10    OOB error = 0.44% 
## -0.1423002 0.05

# Plot the ntry
plot(data.frame(Random_forest_hyperparameter_tune)$mtry,
     data.frame(Random_forest_hyperparameter_tune)$OOBError, 
     type = "l",ylab="OOBError",xlab="mtry", 
     main = "Random Forest mtry tune")

#Hyperparameter in R for random Forest
#ntree: Number of trees to grow.

# Fit the random forest model
rf_model <- randomForest(as.factor(label) ~ y_coordinate + x_coordinate +
                           NDAI + SD + CORR + DF_angle + CF_angle + BF_angle +
                           AF_angle + AN_angle, data = train_data_rf,
                         importance = TRUE,
                         ntree=50,
                         ntry=6,
                         maxnodes= 500, 
                         nodesize = 10)
# Compute the accuracy of the model
predicted.classes_rf <- rf_model %>% predict(test_total[,-3])
mean(predicted.classes_rf == test_total$label)

## [1] 0.9854623

#Other error metrics
#F1 Score
F1_Score(as.numeric(predicted.classes_rf), as.numeric(test_total$label))

## [1] 0.9880215

#Sensitivity
Sensitivity(as.numeric(predicted.classes_rf), as.numeric(test_total$label))

## [1] 0.9949755

# Find the best cut off point
predictions<- data.frame(predict(rf_model, newdata =test_total[,-3], type= "prob" ))
colnames(predictions) <- c(-1,1)
pred <- prediction(predictions$`1`, test_total$label)
perf <- performance(pred, "acc")
plot(perf, avg= "vertical", spread.estimate="boxplot", 
     show.spread.at= seq(0.1, 0.9, by=0.1), 
     main="Random Foret cutoff and performance tradeoff")

index<-which.max(slot(perf, "y.values")[[1]])
max<-slot(perf, "x.values")[[1]][index]
perf <- performance(pred, "tpr", "fpr")
# ROC curve
plot(perf, colorize=T, print.cutoffs.at=c(max), text.adj=c(0.5,0), 
     avg="threshold", lwd=3, main= "Roc curve for Random Forest")
abline(a=0,b=1)

# Feature importance for random forest model
varImp(rf_model)

#CV method 2
cv_result_rf_method2 <- CVgeneric("random forest", 
                                  c("y_coordinate", "x_coordinate", "NDAI", "SD", 
                                    "CORR", "DF_angle", "CF_angle", "BF_angle", 
                                    "AF_angle", 'AN_angle'), 
                                  "label", K, data=method1_train_val, 
                                  loss= accuracy, 
                                  seed)
#Print accuracy result
#cv_result_rf_method2
#train data by using the best training sets
train_data_rf2 = method1_train_val[-cv_result_rf_method2$index, ]

rf_model_method_2 <- randomForest(as.factor(label) ~ y_coordinate + 
                                    x_coordinate + NDAI + SD + CORR +
                                    DF_angle + CF_angle + BF_angle +AF_angle + 
                                    AN_angle,
                                  data = train_data_rf2, 
                                  importance = TRUE,ntree=50,
                                  ntry=6,maxnodes= 500, nodesize = 10)

predicted.classes_rf_method_2 <- rf_model_method_2 %>% predict(test_total[,-3])

4a

# Decision Tree feature importance
ggplot(data=tree_importance, aes(x=reorder(importance, -Overall), y=Overall)) +
  geom_bar(stat="identity", color="black", fill="lightgreen")+
  theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  xlab("features")+
  ylab("importance")+
  ggtitle("Feature importance in Decision Tree")

# Balance the cost complexity and error
fit <- rpart(label~.,
   method="anova", data=train_val)
printcp(fit) # display the results 

## 
## Regression tree:
## rpart(formula = label ~ ., data = train_val, method = "anova")
## 
## Variables actually used in tree construction:
## [1] AN_angle     CORR         NDAI         x_coordinate
## 
## Root node error: 39570/166443 = 0.23774
## 
## n= 166443 
## 
##         CP nsplit rel error  xerror      xstd
## 1 0.643047      0   1.00000 1.00002 0.0011132
## 2 0.034726      1   0.35695 0.35731 0.0022032
## 3 0.020637      2   0.32223 0.32280 0.0021663
## 4 0.020514      3   0.30159 0.29750 0.0019919
## 5 0.016110      4   0.28108 0.28197 0.0018861
## 6 0.010000      5   0.26497 0.26594 0.0019167

plotcp(fit)

summary(fit) 

## Call:
## rpart(formula = label ~ ., data = train_val, method = "anova")
##   n= 166443 
## 
##           CP nsplit rel error    xerror        xstd
## 1 0.64304730      0 1.0000000 1.0000170 0.001113218
## 2 0.03472590      1 0.3569527 0.3573086 0.002203232
## 3 0.02063725      2 0.3222268 0.3228038 0.002166283
## 4 0.02051412      3 0.3015896 0.2974982 0.001991909
## 5 0.01610955      4 0.2810754 0.2819663 0.001886142
## 6 0.01000000      5 0.2649659 0.2659410 0.001916652
## 
## Variable importance
##         NDAI           SD         CORR     AN_angle     AF_angle 
##           25           18           15           15           13 
##     BF_angle x_coordinate     CF_angle     DF_angle 
##           12            1            1            1 
## 
## Node number 1: 166443 observations,    complexity param=0.6430473
##   mean=1.38928, MSE=0.2377412 
##   left son=2 (90667 obs) right son=3 (75776 obs)
##   Primary splits:
##       NDAI         < 0.7947012 to the left,  improve=0.6430473, (0 missing)
##       SD           < 3.090993  to the left,  improve=0.4657505, (0 missing)
##       CORR         < 0.1967055 to the left,  improve=0.4466640, (0 missing)
##       x_coordinate < 229.5     to the right, improve=0.2641900, (0 missing)
##       AN_angle     < 176.5115  to the right, improve=0.2626505, (0 missing)
##   Surrogate splits:
##       SD       < 3.134235  to the left,  agree=0.868, adj=0.709, (0 split)
##       CORR     < 0.1943578 to the left,  agree=0.807, adj=0.576, (0 split)
##       AN_angle < 181.7735  to the right, agree=0.782, adj=0.521, (0 split)
##       AF_angle < 187.0018  to the right, agree=0.762, adj=0.478, (0 split)
##       BF_angle < 245.1032  to the right, agree=0.747, adj=0.444, (0 split)
## 
## Node number 2: 90667 observations,    complexity param=0.02051412
##   mean=1.031831, MSE=0.03081757 
##   left son=4 (89489 obs) right son=5 (1178 obs)
##   Primary splits:
##       AN_angle < 172.3681  to the right, improve=0.2905195, (0 missing)
##       AF_angle < 177.7156  to the right, improve=0.1933133, (0 missing)
##       NDAI     < 0.3928416 to the left,  improve=0.1748651, (0 missing)
##       CORR     < 0.2107458 to the left,  improve=0.1713220, (0 missing)
##       BF_angle < 190.1325  to the right, improve=0.1381122, (0 missing)
##   Surrogate splits:
##       AF_angle     < 178.9558  to the right, agree=0.996, adj=0.671, (0 split)
##       BF_angle     < 189.9061  to the right, agree=0.992, adj=0.397, (0 split)
##       CF_angle     < 199.0912  to the right, agree=0.990, adj=0.199, (0 split)
##       DF_angle     < 208.6219  to the right, agree=0.988, adj=0.102, (0 split)
##       y_coordinate < 21.5      to the right, agree=0.988, adj=0.060, (0 split)
## 
## Node number 3: 75776 observations,    complexity param=0.0347259
##   mean=1.816974, MSE=0.1495277 
##   left son=6 (6936 obs) right son=7 (68840 obs)
##   Primary splits:
##       x_coordinate < 295.5     to the right, improve=0.12127470, (0 missing)
##       CORR         < 0.1886039 to the left,  improve=0.11563010, (0 missing)
##       y_coordinate < 45.5      to the left,  improve=0.07590415, (0 missing)
##       DF_angle     < 261.4745  to the left,  improve=0.06395254, (0 missing)
##       SD           < 2.737522  to the left,  improve=0.05170818, (0 missing)
##   Surrogate splits:
##       AF_angle < 277.8265  to the right, agree=0.909, adj=0.003, (0 split)
##       AN_angle < 263.9707  to the right, agree=0.909, adj=0.002, (0 split)
##       BF_angle < 300.493   to the right, agree=0.909, adj=0.001, (0 split)
##       SD       < 0.5655767 to the left,  agree=0.908, adj=0.000, (0 split)
## 
## Node number 4: 89489 observations
##   mean=1.020975, MSE=0.02053471 
## 
## Node number 5: 1178 observations
##   mean=1.856537, MSE=0.1228817 
## 
## Node number 6: 6936 observations,    complexity param=0.01610955
##   mean=1.392734, MSE=0.2384939 
##   left son=12 (4558 obs) right son=13 (2378 obs)
##   Primary splits:
##       AN_angle     < 195.8774  to the left,  improve=0.3853602, (0 missing)
##       AF_angle     < 211.012   to the left,  improve=0.3482045, (0 missing)
##       BF_angle     < 232.0312  to the left,  improve=0.3265047, (0 missing)
##       y_coordinate < 70.5      to the left,  improve=0.2968674, (0 missing)
##       CF_angle     < 249.6608  to the left,  improve=0.2906712, (0 missing)
##   Surrogate splits:
##       AF_angle     < 209.2239  to the left,  agree=0.946, adj=0.841, (0 split)
##       BF_angle     < 230.8777  to the left,  agree=0.908, adj=0.732, (0 split)
##       CF_angle     < 249.5645  to the left,  agree=0.872, adj=0.625, (0 split)
##       y_coordinate < 77.5      to the left,  agree=0.846, adj=0.552, (0 split)
##       DF_angle     < 265.3024  to the left,  agree=0.843, adj=0.543, (0 split)
## 
## Node number 7: 68840 observations,    complexity param=0.02063725
##   mean=1.859718, MSE=0.1206028 
##   left son=14 (24467 obs) right son=15 (44373 obs)
##   Primary splits:
##       CORR     < 0.1975884 to the left,  improve=0.09836109, (0 missing)
##       SD       < 2.24581   to the left,  improve=0.04381756, (0 missing)
##       AF_angle < 226.7572  to the right, improve=0.04132092, (0 missing)
##       DF_angle < 292.8058  to the left,  improve=0.04096533, (0 missing)
##       NDAI     < 3.208439  to the right, improve=0.03550383, (0 missing)
##   Surrogate splits:
##       AF_angle     < 224.184   to the right, agree=0.719, adj=0.209, (0 split)
##       AN_angle     < 206.027   to the right, agree=0.718, adj=0.207, (0 split)
##       SD           < 3.702372  to the left,  agree=0.705, adj=0.170, (0 split)
##       DF_angle     < 280.584   to the left,  agree=0.691, adj=0.131, (0 split)
##       y_coordinate < 276.5     to the right, agree=0.686, adj=0.116, (0 split)
## 
## Node number 12: 4558 observations
##   mean=1.17376, MSE=0.1435677 
## 
## Node number 13: 2378 observations
##   mean=1.812447, MSE=0.1523766 
## 
## Node number 14: 24467 observations
##   mean=1.713042, MSE=0.2046131 
## 
## Node number 15: 44373 observations
##   mean=1.940595, MSE=0.05587648

rsq.rpart(fit)

## 
## Regression tree:
## rpart(formula = label ~ ., data = train_val, method = "anova")
## 
## Variables actually used in tree construction:
## [1] AN_angle     CORR         NDAI         x_coordinate
## 
## Root node error: 39570/166443 = 0.23774
## 
## n= 166443 
## 
##         CP nsplit rel error  xerror      xstd
## 1 0.643047      0   1.00000 1.00002 0.0011132
## 2 0.034726      1   0.35695 0.35731 0.0022032
## 3 0.020637      2   0.32223 0.32280 0.0021663
## 4 0.020514      3   0.30159 0.29750 0.0019919
## 5 0.016110      4   0.28108 0.28197 0.0018861
## 6 0.010000      5   0.26497 0.26594 0.0019167

Diagnostics 4b

#refer section 4d

Diagnostics 4c AdaBoost

# Long running time 
# Fit adaboost model
adaboost<- adaboost(label~y_coordinate + x_coordinate  + NDAI + SD + CORR + DF_angle + CF_angle+ BF_angle + AF_angle + AN_angle ,data=train_val, nIter=10)
# Adaboost model Accuracy 
mean(predict(adaboost, newdata =test_total[,-3] )$class == test_total$label)

## [1] 0.9976211

# Fit adaboost model without geographic information 
boost_no_xy<- boosting(label~NDAI + SD + CORR + DF_angle + CF_angle+ BF_angle + AF_angle + AN_angle ,data=train_val, mfinal = 10, boos=TRUE)

#Error based on number of trees 
errorevol(boost_no_xy,train_val[,c(-1,-2)])->evol.train
errorevol(boost_no_xy,test_total[,c(-1,-2)])->evol.test
plot.errorevol(evol.test,evol.train)

importanceplot(boost_no_xy)

# Margins
BC.margins<-margins(boost_no_xy,train_val[,c(-1,-2)]) # training set
BC.adaboost.pred <- predict.boosting(boost_no_xy,newdata=test_total[,c(-1,-2)])
 
BC.predmargins<-margins(BC.adaboost.pred,test_total[,c(-1,-2)]) # test set
plot.margins(BC.predmargins,BC.margins,alpha=0.3 )

# Adaboost model Accuracy without geographic information 
mean(predict(boost_no_xy, newdata =test_total[,-3] )$class == test_total$label)

## [1] 0.9297386

# Cut off point selection 
predictions<- data.frame(predict(adaboost, newdata =test_total[,-3] )$prob)
colnames(predictions) <- c(-1,1)
pred <- prediction(predictions$`1`, test_total$label)
perf <- performance(pred, "acc")
plot(perf, avg= "vertical", spread.estimate="boxplot", 
     show.spread.at= seq(0.1, 0.9, by=0.1), 
     main="Adaboosting cutoff and performance tradeoff")

index<-which.max(slot(perf, "y.values")[[1]])
max<-slot(perf, "x.values")[[1]][index]
# ROC curve
perf <- performance(pred, "tpr", "fpr")
plot(perf, colorize=T, print.cutoffs.at=c(max), text.adj=c(0.3,0), 
     avg="threshold", lwd=3, main= "Adaboosting curve for ROC")
abline(a=0,b=1)

Diagnostics 4d

#Misclassification pattern for random forest 
mis_label_random_forest<- train_data_rf[predicted.classes_rf!=test_total$label,]
ggplot(mis_label_random_forest)+ geom_point(alpha = 0.5, 
                                            aes(x= x_coordinate, y = y_coordinate,
                                                color = factor(label)))+
  xlab(" x coordinate") + ylab(" y coordinate") + 
  ggtitle("Mis-classification in random-forest model split method 1 ")+ 
  theme_bw()

Predicted <-predicted.classes_rf
Actual<- test_total$label
fourfoldplot(table(Predicted, Actual))

#Method 2
mis_label_random_forest<- train_data_rf2[predicted.classes_rf_method_2!=test_total$label,]
ggplot(mis_label_random_forest)+ geom_point(alpha = 0.5, 
                                            aes(x= x_coordinate,y = y_coordinate,
                                                color = factor(label)))+
  xlab(" x coordinate") + ylab(" y coordinate")+
  ggtitle("Mis-classification in random-forest model split method 2 ")+theme_bw()

Predicted <-predicted.classes_rf_method_2
fourfoldplot(table(Predicted, Actual))

#Misclassification pattern for decision tree
mis_label_decision_tree<- train_data[tree.pred2!=test_total$label,]
ggplot(mis_label_decision_tree)+ geom_point(alpha = 0.5, 
                                            aes(x= x_coordinate, y = y_coordinate,
                                                color = factor(label)))+
  xlab(" x coordinate") + ylab(" y coordinate") + 
  ggtitle("Mis-classification in decision tree model split method 1 ")+ theme_bw()

Predicted <-tree.pred2
fourfoldplot(table(Predicted, Actual))

#Method 2
mis_label_decision_tree2<- train_data_tree_2[tree.pred_method_2!=test_total$label,]
ggplot(mis_label_decision_tree2)+ geom_point(alpha = 0.5, aes(x= x_coordinate, y = y_coordinate, color = factor(label)))+
  xlab(" x coordinate") + ylab(" y coordinate") + 
  ggtitle("Mis-classification in decision tree model split method 2 ")+ theme_bw()

Predicted <-tree.pred_method_2
fourfoldplot(table(Predicted, Actual))